return_sequence=False,
sum_out=True)
# model initialization
seq1 = Sequential([
LookupTable(vocab_size, embed_size, init, update=True, pad_idx=pad_idx),
rlayer,
Affine(init, activation=Softmax(), bias_init=init, axes=(ax.Y, ))
])
optimizer = RMSProp(decay_rate=0.95,
learning_rate=2e-3,
epsilon=1e-6,
gradient_clip_value=gradient_clip_value)
output_prob = seq1.train_outputs(inputs['review'])
loss = ng.cross_entropy_multi(output_prob,
ng.one_hot(inputs['label'], axis=ax.Y),
usebits=True)
mean_cost = ng.mean(loss, out_axes=[])
updates = optimizer(loss)
train_outputs = dict(batch_cost=mean_cost, updates=updates)
loss_outputs = dict(cross_ent_loss=loss)
# Now bind the computations we are interested in
transformer = ngt.make_transformer()
train_computation = make_bound_computation(transformer, train_outputs, inputs)
loss_computation = make_bound_computation(transformer, loss_outputs, inputs)
cbs = make_default_callbacks(output_file=args.output_file,
Affine(axes=ax.Y, weight_init=init_uni, activation=Softmax())
])
######################
# Input specification
ax.C.length, ax.H.length, ax.W.length = train_set.shapes['image']
ax.D.length = 1
ax.N.length = args.batch_size
ax.Y.length = 10
# placeholders with descriptive names
inputs = dict(image=ng.placeholder([ax.C, ax.H, ax.W, ax.N]),
label=ng.placeholder([ax.N]))
optimizer = GradientDescentMomentum(0.01, 0.9)
output_prob = seq1.train_outputs(inputs['image'])
errors = ng.not_equal(ng.argmax(output_prob, out_axes=[ax.N]), inputs['label'])
loss = ng.cross_entropy_multi(output_prob,
ng.one_hot(inputs['label'], axis=ax.Y))
mean_cost = ng.mean(loss, out_axes=())
updates = optimizer(loss)
train_outputs = dict(batch_cost=mean_cost, updates=updates)
loss_outputs = dict(cross_ent_loss=loss, misclass_pct=errors)
# Now bind the computations we are interested in
transformer = ngt.make_transformer()
train_computation = make_bound_computation(transformer, train_outputs, inputs)
loss_computation = make_bound_computation(transformer, loss_outputs, inputs)
cbs = make_default_callbacks(output_file=args.output_file,
])
# Bind axes lengths:
ax.Y.length = len(tree_bank_data.vocab)
ax.REC.length = time_steps
ax.N.length = args.batch_size
# placeholders with descriptive names
inputs = dict(inp_txt=ng.placeholder([ax.REC, ax.N]),
tgt_txt=ng.placeholder([ax.REC, ax.N]))
optimizer = RMSProp(decay_rate=0.95,
learning_rate=2e-3,
epsilon=1e-6,
gradient_clip_value=gradient_clip_value)
output_prob = seq1.train_outputs(inputs['inp_txt'])
loss = ng.cross_entropy_multi(output_prob,
ng.one_hot(inputs['tgt_txt'], axis=ax.Y),
usebits=True)
mean_cost = ng.mean(loss, out_axes=[])
updates = optimizer(loss)
train_outputs = dict(batch_cost=mean_cost, updates=updates)
loss_outputs = dict(cross_ent_loss=loss)
# Now bind the computations we are interested in
transformer = ngt.make_transformer()
train_computation = make_bound_computation(transformer, train_outputs, inputs)
loss_computation = make_bound_computation(transformer, loss_outputs, inputs)
cbs = make_default_callbacks(output_file=args.output_file,